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Particle filter for mobile station Positioning in a cellular network

机译：蜂窝网络中用于移动台定位的粒子滤波器

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摘要

Global Positioning System is the most commonly employed localization technique to localize mobile devices in outdoor environments. However, this cannot be operating when the line-of-sight visibility to the satellites is lost, as in indoor, dense environments or bad weather conditions. This motivates the growing network or hybrid based positioning techniques that use signal strength and network topology. Our methodology uses TEMS Investigation software to retrieve network information including signal strength and cell-identities of various network transmitters, and a nonlinear estimation like technique to estimate the mobile position. Typically, under linearity and Gaussian additive noise constraint, the conventional Kalman filters yields optimal estimation solution, provided the noise statistics is known. However, when such constraint is violated, e.g., either the measurement or state model is non-linear, the convergence of the filter cannot be granted. In this paper, we present a suboptimal estimation method using the particle filter where the cellular network data are combined to yield a close to optimal solution. The algorithm is tested on synthetic and real word dataset, where the results are compared with conventional Kalman filtering and unscented transform, where the superiority of the particle filtering like approach is demonstrated.
机译：全球定位系统是在室外环境中对移动设备进行本地化的最常用的本地化技术。但是，如果在室内，茂密的环境或恶劣的天气条件下，失去了对卫星的视线可见性，此功能将无法运行。这激励了使用信号强度和网络拓扑的不断发展的网络或基于混合的定位技术。我们的方法使用TEMS Investigation软件来检索网络信息，包括各种网络发射机的信号强度和小区标识，以及使用非线性估计之类的技术来估计移动位置。通常，在线性和高斯加性噪声约束下，如果已知噪声统计信息，则传统的卡尔曼滤波器会产生最佳估计解。但是，当违反了这样的约束时，例如，测量模型或状态模型是非线性的，就不能保证滤波器的收敛性。在本文中，我们提出了一种使用粒子滤波器的次优估计方法，该方法结合了蜂窝网络数据以产生接近最佳的解决方案。该算法在合成词和实词数据集上进行了测试，将结果与传统的卡尔曼滤波和无味变换进行了比较，从而证明了类似粒子滤波的方法的优越性。

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